Plots.jl gives Julia one plotting syntax that can target several rendering backends. Install it with the package manager, start with the GR backend, and switch to PlotlyJS, PythonPlot, PGFPlotsX or UnicodePlots when your display, interactivity or publication workflow requires something different.
What Plots.jl is—and what it is not
Plots.jl is a high-level, backend-agnostic plotting API. Your Julia code describes data and plot attributes; a backend then renders the result for a window, notebook, terminal or file:
Julia data → Plots.jl command → backend → displayed or exported figure
The default installation uses GR, but the same plotting commands can target other renderers. This is syntax portability, not guaranteed visual or feature equivalence: an attribute supported by one backend may be ignored, approximated or unavailable in another. The backend documentation lists these differences.
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Plots.jl also has a recipe system. A package can define how a specialised data type should be plotted, so users can call the familiar API without learning a separate plotting language; the design is discussed in the Plots.jl recipe-system paper.
Install Plots.jl and make a first figure
In Julia’s REPL, VS Code, Pluto or Jupyter, run:
import Pkg
Pkg.add("Plots")
using PlotsGR is included and selected by default in a normal installation. The first plotting call can be slower than later calls while packages and backend resources initialise.
x = range(0, 10, length=100)
y = sin.(x)
plot(x, y)The dot in sin.(x) broadcasts the function over every element. Calling sin(x) with an array or range instead is a common source of a method error. The current official tutorial is generated with Julia 1.12.6; avoid assuming that development-branch examples and stable documentation have identical configuration names.
Common chart types
Plots.jl provides concise functions for standard scientific and exploratory charts. The GR gallery demonstrates these and additional forms such as polar plots and annotations.
plot(x, y) # line (optionally with markers)
scatter(x, y) # points
bar(["A", "B", "C"], [12, 19, 7])
histogram(randn(1_000))
heatmap(rand(20, 20))
contour(x, x, [sin(a) * cos(b) for a in x, b in x])
surface(x, x, [sin(a) * cos(b) for a in x, b in x])For a bar chart, categorical labels and values must have matching lengths. Heatmap, contour and surface inputs need dimensions that agree with the selected backend’s interpretation of the coordinate arrays. Dates, categorical values, missing and NaN are supported in many cases, but their handling can vary by backend.
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Build and customise a complete chart
using Plots
x = range(0, 2π, length=200)
y1 = sin.(x)
y2 = cos.(x)
plot(
x, y1;
label = "sin(x)",
linewidth = 2,
xlabel = "x",
ylabel = "value",
title = "Sine and cosine",
legend = :topright,
)
plot!(
x, y2;
label = "cos(x)",
linestyle = :dash,
)labelsupplies a legend entry; uselabel=falseto suppress one.linewidth,linestyle, marker attributes and colours control appearance.xlabel,ylabelandtitleexplain the axes and subject.plot!modifies the current plot instead of creating a separate figure.
A semicolon before keyword arguments is idiomatic Julia syntax, not a Plots.jl requirement. Attributes are ultimately limited by the selected backend.
Add several data series
For equally sized series, a matrix is a compact option:
plot(x, [sin.(x) cos.(x)])For explicit control over labels and the plot object, use:
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p = plot(x, sin.(x), label="sin")
plot!(p, x, cos.(x), label="cos")Check dimensions before plotting matrix data. A vector of vectors is not necessarily interpreted like a matrix, and automatic labels may not communicate which column is which. Supply labels whenever the chart will be shared or published.
Arrange subplots with a layout
p1 = plot(x, sin.(x), title="Sine", label=false)
p2 = plot(x, cos.(x), title="Cosine", label=false)
p3 = scatter(rand(25), title="Scatter", label=false)
p4 = histogram(randn(500), title="Distribution", label=false)
plot(p1, p2, p3, p4; layout=(2, 2), size=(900, 650))layout=(2, 2) requests two rows and two columns. Titles and labels set while creating p1 through p4 belong to those subplots; attributes supplied in the final combination can apply globally, depending on the attribute and backend. Exact spacing and pixel composition are not identical across renderers.
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Choose a rendering backend
| Backend | Use it for | Trade-off or requirement |
|---|---|---|
| GR (default) | General-purpose static scientific and exploratory plots | Less naturally interactive than browser-oriented backends |
| Plotly | Browser graphics with hover interaction | Different capability set from PlotlyJS |
| PlotlyJS | Richer interactive graphics, Jupyter and standalone HTML | Additional frontend resources and export considerations |
| PythonPlot | Matplotlib-oriented Python workflows from Julia | Introduces Python-side ecosystem considerations |
| PGFPlotsX | LaTeX-native publication figures | Requires a LaTeX installation |
| UnicodePlots | SSH, terminals and headless machines | Lower visual fidelity than graphical output |
Select a backend explicitly when reproducibility or environment support matters:
gr()
plotly()
plotlyjs()
import Pkg
Pkg.add("PythonPlot")
using Plots
pythonplot()
Pkg.add("PGFPlotsX")
pgfplotsx()
Pkg.add("UnicodePlots")
unicodeplots()plotly() and plotlyjs() are separate choices. PlotlyJS’s Julia documentation covers interactive Jupyter output, standalone HTML and Dash.jl applications at plotly.com/julia/getting-started.
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p = plot(x, sin.(x); size=(800, 500), dpi=150)
savefig(p, "figure.png")
savefig(p, "figure.svg")
savefig(p, "figure.pdf")With a current plot, savefig("figure.png") is also supported. Formats are backend-dependent: GR commonly produces raster and vector files, while interactive Plotly workflows naturally deliver HTML. PlotlyJS documents PDF, HTML, JSON, PNG, SVG, JPEG and WebP extensions at its saving guide. Always open the actual exported file and check dimensions, fonts, clipping, transparency, annotations and whether output was rasterised.
Display in the REPL, VS Code, Jupyter or Pluto
- REPL: the configured display may open a window or show an inline representation.
- VS Code: a compatible backend can render in the plot pane; PythonPlot or Plotly may be useful when GR’s display is unsuitable.
- Jupyter/IJulia and Pluto: plots can render inline when the backend and frontend integration are available.
- Headless servers: export directly or use
unicodeplots()for terminal output.
If PlotlyJS installs but its graphics do not appear, rebuild its local resources:
import Pkg
Pkg.build("PlotlyJS")This recovery step is documented by Plotly’s Julia guide.
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Set defaults and themes
Prefer local attributes when a setting belongs to one figure:
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plot(x, y;
size=(800, 500),
dpi=150,
legend=false,
framestyle=:box,
)For persistent settings, the stable installation guide supports this in ~/.julia/config/startup.jl:
ENV["PLOTS_DEFAULT_BACKEND"] = "PlotlyJS"
PLOTS_DEFAULTS = Dict(
:markersize => 10,
:legend => false,
)Development documentation at docs.juliaplots.org/dev/install contains newer PlotsBase-related examples; do not copy those names into a stable setup without checking the version you use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Extensions for specialised data
Ordinary lines, scatter plots, bars and histograms do not require extensions. Add one when its recipes match your data:
import Pkg
Pkg.add("StatsPlots")
Pkg.add("GraphRecipes")- StatsPlots.jl adds recipes aimed at statistical workflows.
- GraphRecipes.jl adds graph and network-oriented plotting recipes.
Troubleshoot common failures
No visible plot
- Confirm
using Plotscompleted without an error. - Select a known backend, for example
gr(). - Try exporting:
savefig("test.png"). A valid file separates rendering from frontend display problems. - On a terminal or headless host, try
unicodeplots(). - For PlotlyJS, run
Pkg.build("PlotlyJS").
A keyword has no effect
Enable warnings with warn_on_unsupported=true and consult the backend support table. A high-level keyword can be accepted while the renderer ignores or approximates it.
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plot(x, y; warn_on_unsupported=true)GR or LaTeX dependencies fail
Some Linux GR setups need system packages described through the official installation instructions. PGFPlotsX requires a working LaTeX installation; it is not a dependency-free route to PDF.
The export differs from the preview
Inspect the saved file rather than relying on the notebook image. Backend-specific fonts, margins, marker support, transparency and vector/raster decisions can change the result.
Plots.jl or Makie?
Plots.jl is a strong starting point when you want one concise API for common charts and the ability to change renderers. Makie is a separate, high-performance Julia visualisation ecosystem with packages such as GLMakie and CairoMakie. Consider Makie when you need complex layouts, reactive scenes or finer-grained composition and are willing to learn its different model. Neither package is universally faster or better; the choice depends on interactivity, layout control, output format and API preference.
Further reading and next steps
Begin with GR, make a complete plot, export it, then switch backends for the requirement that motivated the change. Keep the data dimensions explicit, label series deliberately and verify the exported artifact. The official tutorial is at docs.juliaplots.org/latest/tutorial, with backend details at docs.juliaplots.org/latest/backends.
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